Prof Lei He

UCLA

EE 201C Modeling of VLSI Circuits and Systems

EE 201C Modeling of VLSI Circuits and Systems

Chapter 5 Signal and Power IntegrityChapter 5 Signal and Power Integrity

On-chip signal integrity RC and RLC coupling noise

Power integrity Static noise: IR drop Dynamic noise: L di/dt noise

Beyond die noise (chapter 6) In-package decap insertion Low frequency P/G resonance Noise for High-speed signaling

Reading on Power IntegrityReading on Power Integrity

Static noise: IR drop S. Tan and R. Shi, “Optimization of VLSI Power/Ground (P/G)

Networks Via Sequence of Linear Programmings”, DAC’09

Dynamic noise: L di/dt noise Yiyu Shi, Jinjun Xiong, Chunchen Liu and Lei He, "Efficient

Decoupling Capacitance Budgeting Considering Current Correlation Including Process Variation", ICCAD, San Jose, CA, Nov. 2007.

Supplementary reading: H. Qian, S. R. Nassif, and S. S. Sapatnekar, “Power Grid

Analysis Using Random Walks,” IEEE Trans. on CAD, 2005. Yiyu Shi, Wei Yao, Jinjun Xiong, and Lei He, "Incremental and

On-demand Random Walk for Iterative Power Distribution Network Analysis", ASPDAC 2009

Efficient Decoupling Capacitance Budgeting Considering Operation and Process VariationsEfficient Decoupling Capacitance Budgeting

Considering Operation and Process Variations

Yiyu Shi*, Jinjun Xiong+, Chunchen Liu* and Lei He**Electrical Engineering Department, UCLA

+IBM T. J. Watson Research Center, Yorktown Heights, NY

This work is partially supported by NSF CAREER award and

a UC MICRO grant sponsored by Altera, RIO and Intel.

What is Decap?What is Decap?

Decap: decoupling capacitor

Static IR Drop:

V = I × R

= (P /Vdd ) × R Dynamic IR Drop :

V = L di/dt noise

Why Adding Decap is Important?Why Adding Decap is Important?

Power source fluctuations increase significantly

Illustration of voltage drop variation of modern VLSI chip

Introduction – Voltage Drop Impacts on Timing

10% voltage drop can cause more than 10% delay

Introduction - Effect of Adding DecapsIntroduction - Effect of Adding Decaps

Adding decaps is the most effective way to reduce voltage noises in P/G grids

Introduction - The Costs of Adding DecapsIntroduction - The Costs of Adding Decaps Decaps are mainly made of MOS

gate capacitors Consuming premium white spaces

White space can otherwise be used for adding buffers, other logic gates for physical optimization.

MOS gates are leaky or become more leaky with scaling More leakage powers

Excessive decaps will lead to low yield and low circuit resonant frequency, etc.

Economic use of decaps is important!!

Decap Budgeting OverviewDecap Budgeting Overview

Nodes away from Vdd pin may suffer from supply noise due to sudden burst of activity

Provide current for surplus need from the local storage charge

Side effect of adding too much decap

Increased leakage Increased die area Risk of yield loss

Location matters The closer to the turbulent

point, the more noise reduction can be achieved

Given the amount of decap to be inserted, find the optimal location so that the noise can be suppressed to a maximum extent.

decap

Vn

t0 t1

intrinsiccap

Load current

power supply

We define the noise as the integral over time of the area belowU

U

Decap Budgeting Problem FormulationDecap Budgeting Problem Formulation

Objective Find the distribution and location of the white space so the

noise on power network is minimized

Constraints: Local decap constraintsLocal decap constraints: amount of decap allowed at each location is

limited due to placement constraint Global decap constraintsGlobal decap constraints: total amount of decap allowed is limited due

to leakage constraint

Limitation of existing work: Most existing work in essence uses worst case load current in order to

guarantee there is no noise violation, which is too pessimistic It is not clear how to provide decap budgeting solution that is robust to

current loads under all kinds of operations for a circuit

Major Contribution of our workMajor Contribution of our work

In this paper, we develop a novel stochastic model for current loads, taking into account operation variation such as temporal and logic-induced correlations and process variations such as systematic and random Leff variation.

We propose a formal method to extract operation variation and formulate a new decap budgeting problem using the stochastic current model.

We develop an effective yet efficient iterative alternative programming algorithm and conduct experiments using industrial designs.

Experiments show that considering both operation and process variations can reduce over-design significantly. This demonstrates the importance of considering operation variation.

OutlineOutline

Stochastic Modeling and Problem Formulation

Algorithm

Experimental Results

Conclusions

Correlated Load CurrentsCorrelated Load Currents

Strong correlation between load currents due to Operation variation

Currents at different ports have logic-induced correlation– Large number of ports with limited control bits– Currents at certain ports cannot reach maximum at the same time due to the

inherent logic dependency for a given design Currents at the same port have temporal correlation

– System takes several clock cycles to execute one instruction– The currents cannot reach maximum at all the clock cycles

Process variation Currents have intra-die variation due to process variation

– The P/G network is robust to process variation, but the load currents have intra-die variation because the circuit suffers from process variation.

– Leff variation is one of the primary variation sources and the variation is spatially correlated [Cao:DAC’05]

Current Sampling Current Sampling

Model the current in each clock cycle as a triangular waveform and assume constant rising/falling time Other current waveforms can be used. It will not affect the algorithm In our verification, we use the detailed non-simplified current waveform

Partition a circuit into blocks and assume no correlation between different blocks [Najm:ICCAD’05]

Extensive simulation for each block to get the peak current value in each clock cycle and at each port.

Assume there is only temporal correlation within certain number of clock cycles L L can be the number of clock cycles to execute certain function

Stochastic Current ModelingStochastic Current Modeling Divide peak current values into different sets according to the clock

cycle and port number The set contains peak current values at port k and in clock cycle j,

j+L, j+2L,… Example: Take L=2, and consider two ports in 8 consecutive clock cycles

Define to be the stochastic variable with the sample set For example, has the samples 0.1, 0.3, 0.5, 0.7, and therefore has

mean value 0,4 The correlation between and reflects the temporal correlation

between clock cycle j1 and j2 The correlation between and reflects the logic induced correlation

between port k1 and k2.

jkB

11b

11B

1jkB

2jkB

jkB 1

jkB 2

1 2 3 4 5 6 7 8

0 .1 0 .2 0 .3 0 .4 0 .5 0 .6 0 .7 0 .8

0 .01 0 .02 0 .03 0 .04 0 .05 0 .06 0 .07 0 .08

c lo ckcyc lep o rt 1

p o rt 2

0 .1 0 .3 0 .5 0 .7

0 .2 0 .4 0 .6 0 .8

0 .01 0 .03 0 .05 0 .07

0 .02 0 .04 0 .06 0 .08

21b12b22b

jkb

clock cycles j, temporal correlation

port k, logic-induced correlation

Extraction of CorrelationsExtraction of Correlations The logic-induced correlation coefficient between port k1 and k2 at

clock cycle j can be computed as

Temporal correlation coefficient between clock cycle j1 and j2 at port k can be computed as

To take process variation into consideration, sample each multiple times over different region, and the above two formulas can still be applied

),1(,)()(

),cov();,( 2121 21

21

LjjBB

BBkjj j

kjk

jk

jk

jkB

),1(,)()(

),cov(),;( 2121

21

21 pkkBB

BBkkj

jk

jk

jk

jk

)(~ 8.05.0tddoxeff VVtLI

Extraction of CorrelationsExtraction of Correlations As is not Gaussian, apply Independent Component

Analysis [Hyvarinen’01] to remove the correlation between and get a new set of independent variables r1, r2, … Each can be represented by the linear combination of r1, r2,

… Accordingly the waveform at each clock cycle can be

reconstructed from those r1,r2,…, i.e.,

The new variables ri catch both the operation and process variations.

jkB

jkB

jkB

Example of Extracted Temporal CorrelationExample of Extracted Temporal Correlation

The correlation map for peak currents between different clock cycles of one port from an industry application.

The P/G network is modeled as RC mesh The load currents are obtained by detailed simulation of the circuit

It can be seen that the correlation matrix can be clearly divided into four trunks, and L can be set as 10

Parameterized MNA FormulationParameterized MNA Formulation

Original MNA formulation

With the design variables - decap area wi, the G, C matrices can be expressed as

Together with the stochastic current model, the MNA formulation becomes:

With parameters wi and ri

The objective now is to find the optimal solution for those parameters More specifically, find the wi values that minimize the noise with the ri

corresponding to the load currents which introduce the maximum noise

Stochastic Decap FormulationStochastic Decap Formulation

Minimize the maximum noise sum over all ports Subject to the stochastic current variable upper/lower bound

Subject to Local decap area constraint due to placement constraint Global decap area constraint due to leakage constraint

Non-convex min/max optimization problem Difficult to find global optimal solution

M

ii

ii

kkk

p

ikii

rw

Ww

Miwwts

qkrrr

dttrwyUf

P

ik

i

1

1

10..

1,

));,((supmin

)1(

OutlineOutline

Stochastic Modeling and Problem Formulation

Algorithm

Experimental Results

Conclusions

Iterative Programming AlgorithmIterative Programming Algorithm

Find the optimal decap budgeting for the giving max droop/bounce

update the max droop/bounce

update the decap budgeting

Find the input corresponding to the max. droop/bounce for the given decap budgeting

Cannot guarantee optimality, but can guarantee convergence and efficiency

Experimental results show our algorithm can achieve good optimization results

Each iteration we increase the white space allowed until all the white space has been used up or it converges

M

ii

ii

kkk

p

ikii

rw

Ww

Miwwts

qkrrr

dttrwyUf

P

ik

i

1

1

10..

1,

));,((supmin

)1(

Illustration of Iterative ProgrammingIllustration of Iterative Programming

A0: Initial noise curve at one randomly selected port

A1: The noise curve under the optimal decap budgeting for a giving droop/bounce

A2: The noise curve with the input corresponding to the max. droop/bounce for the decap budgeting in A1

A3: The noise curve under the optimal decap budgeting for the giving max droop/bounce in A2

A0: Initial

A1: (P3)

A2: (P2)

A3: (P3)

Sequential ProgrammingSequential Programming

We apply sequential linear programming (sLP) to solve each of the two sub-problems. For each sub-problem, we iteratively do the following two steps until the solution converges: Compute the sensitivities of all the variables to the first order by moment

matching.

Linearize the objective function with the sensitivities and the optimization problem becomes an LP

BuxCwCsxGwGM

iiwi

M

iiwi

1,

1, )()(

M

iii wxx

10

BuxsCG 0)(0,, )()( xsCGsCG iwiwi

first order sensitivities

M

iii w

1

min(max)

OutlineOutline

Stochastic Modeling and Problem Formulation

Algorithm

Experimental Results

Conclusions

Impact of Current CorrelationsImpact of Current Correlations

Model 1 Maximum current at all ports

Model 2 Stochastic model with logic-induced correlation

Model 3 Model 2 + temporal correlation

Compared with the model assuming maximum currents at all ports, under the same decap area, Stochastic model with spatial correlation only reduce the noise by up to 3X Stochastic model with both spatial and temporal correlation reduce the noise by up to 9X

Node # Noise (V*s) Runtime (s)

Model 1 Model 2 Model 3 Model 1 Model 2 Model 3

1284 6.33e-7 1.28e-7 4.10e-8 104.2 161.2 282.3

10490 5.21e-5 1.09e-5 4.80e-6 973.2 1430 2199

42280 7.92e-4 5.38e-4 9.13e-5 2732 3823 5238

166380 1.34e-2 5.37e-3 2.28e-3 3625 5798 7821

avg 1 1/2.68X 1/9.10X 1 1.50X 2.26X

Impact of Leff VariationImpact of Leff Variation

Compared with the stochastic model without considering Leff variation, the stochastic model with it reduce the average noise by up to 4X and the 3-sigma noise by up to 13X

Node #3429 3.06X

V.R. sLP sLP + Leff

mean (V*s)

std (V*s)

runtime (s)

mean (V*s)

std (V*s)

runtime (s)

1284 10% 9.28e-7 3.97e-7 184.2 6.14e-7 1.38e-7 332.8 1.81X20% 9.43e-7 4.55e-7 6.38e-7 1.86e-7

10490 10% 1.03e-4 4.79e-5 1121 7.22e-5 1.23e-5 3429 3.06X20% 1.22e-4 4.38e-5 7.94e-5 2.06e-5

42280 10% 2.29e-3 9.72e-4 2236 8.23e-4 1.01e-4 6924 3.10X20% 4.43e-3 1.01e-3 8.28e-4 1.92e-4

166380 10% 2.06e-2 9.91e-3 3824 5.31e-3 8.92e-4 11224 2.93X20% 2.31e-2 1.03e-2 5.92e-3 9.33e-4

avg 10% 1 1 1 1/2.02X 1/5.05X 2.73X

20% 1 1 1/1.95X 1/4.05X

ConclusionsConclusions

In this paper, we develop a novel stochastic model for current loads, taking into account operation variation such as temporal and logic-induced correlations and process variations such as systematic and random Leff variation.

We propose a formal method to extract operation variation and formulate a new decap budgeting problem using the stochastic current model.

We develop an effective yet efficient iterative alternative programming algorithm and conduct experiments using industrial designs. Experimental results show that the noise can be reduced by up to 9X.

We also apply similar idea to temperature-aware clock routing [Hao:ispd’07] and microprocessor floorplanning (ICCAD’07).